Paper Citation Record · LEDGER

Transformers learn to implement preconditioned gradient descent for in-context learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2306.00297.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2306.00297 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:10:16.045295Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation aac2ec61-0150-425d-8f2f-93bfde952cdb · inbound

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data cites this paper.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:35:02.224323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b186e2f3-97c6-49a0-869d-367bed3f96c2 · inbound

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization cites this paper.

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T06:10:16.045295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6cbe232-4d98-48c7-936f-e77ddd0529c8 · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:49.045950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58ad0626-7d4a-4262-944a-da690d3735cf · inbound

How Can Mamba Learn In Context with Outliers and Generalize Provably? cites this paper.

How Can Mamba Learn In Context with Outliers and Generalize Provably? Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T13:29:28.724747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3684bad-8f82-40d9-972a-943f61ef96f4 · inbound

Spectral Transformer Neural Processes cites this paper.

Spectral Transformer Neural Processes Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:41:34.636095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2bed31f6-a579-419b-be4c-c6ce94c9520a · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:56:32.025783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aafe8a90-bed3-48a2-94b6-eeaffa8cf47b · inbound

Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models cites this paper.

Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:12:34.786403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4594d1cc-0e0d-40f7-94b7-b3a916a35502 · inbound

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents cites this paper.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T22:15:05.526499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.